TY - GEN
T1 - Constellate
T2 - 33rd IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2025
AU - Rachuri, Sri Pramodh
AU - Gandhi, Anshul
AU - Jung, Gueyoung
AU - Narayanan, Shankaranarayanan Puzhavakath
AU - Zelezniak, Alex
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - As the adoption of Virtualized Radio Access Networks (vRAN) is gaining momentum in 5 G networks, Mobility Network Operators are considering a Centralized RAN (CRAN) architecture that moves the baseband functions to a far-edge cloud in order to gain dimensioning flexibility, resiliency and improved RAN performance. However, there have been limited studies on the benefits of centralization in improving RAN compute utilization, especially in the context of pooling the compute-intensive Distributed Unit (DU) resources. In this paper, we present the first study on the benefits of pooling in improving DU server utilization. Using longitudinal traces from a real-world 5G network, we show that significant Capex and Opex gains of 84% and 9 4%, respectively, can be obtained through fine-grained pooling at a granularity of 1 second. We also present an affinitybased and dynamic pooling algorithm that can reduce the pooling overheads while still achieving significant pooling gains.
AB - As the adoption of Virtualized Radio Access Networks (vRAN) is gaining momentum in 5 G networks, Mobility Network Operators are considering a Centralized RAN (CRAN) architecture that moves the baseband functions to a far-edge cloud in order to gain dimensioning flexibility, resiliency and improved RAN performance. However, there have been limited studies on the benefits of centralization in improving RAN compute utilization, especially in the context of pooling the compute-intensive Distributed Unit (DU) resources. In this paper, we present the first study on the benefits of pooling in improving DU server utilization. Using longitudinal traces from a real-world 5G network, we show that significant Capex and Opex gains of 84% and 9 4%, respectively, can be obtained through fine-grained pooling at a granularity of 1 second. We also present an affinitybased and dynamic pooling algorithm that can reduce the pooling overheads while still achieving significant pooling gains.
KW - 5G
KW - DU pooling
KW - RAN utilization
KW - Virtualized RAN
UR - https://www.scopus.com/pages/publications/105031717378
U2 - 10.1109/MASCOTS67699.2025.11283390
DO - 10.1109/MASCOTS67699.2025.11283390
M3 - Conference contribution
AN - SCOPUS:105031717378
T3 - Proceedings - IEEE Computer Society's Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS
BT - Proceedings - 2025 IEEE 33rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2025
PB - IEEE Computer Society
Y2 - 21 October 2025 through 23 October 2025
ER -